This invention relates to the field of computer vision.
Machine learning techniques are used in a variety of tasks relevant to computer vision applications. A machine learned system can analyze a visual scene captured in a digital image and cluster data to find patterns, as well as detect and classify visual features. In the latter application, portions of an image, or features, are typically extracted, analyzed, and classified in an iterative process that ‘teaches’ the machine to classify of features extracted from newly provided images based on predictive analysis, thereby allowing the computer to ‘see’ these features and identify them.
Machine learning techniques fall into several general categories, including both supervised and unsupervised learning. Supervised learning is based on labeled inputs that provide the computer with the desired output value during the learning process; whereas unsupervised learning is based on unlabeled inputs and allow the computer to independently find structure in the input data.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.